Data Platform
Proximity
Native (own / self-hosted weights)
Models
proprietary / self-built modelsMeta · LlamaOpenAI · GPT
Cloud · datastore
Multi-CloudDelta Lake (transactional lakehouse tables on cloud object storage)[6][10]Apache Spark (distributed compute engine for ETL/ML/streaming)[11][12]Unity Catalog (centralized governance and metadata layer)[1][5]Cloud object storage (Amazon S3, Azure Data Lake Storage, Google Cloud Storage) as primary data store[6][4][12]Medallion (Bronze/Silver/Gold) data modeling pattern on top of Delta tables[2][12]
Compliance
Separation of control plane (Databricks-managed backend services) and compute/data plane (customer cloud account) for isolation and governance[3][4][7][11]Network isolation via VPC/VNet injection, CIDR planning, and private connectivity for data plane resources[1][5][7]Workspace- and catalog-level access control with Unity Catalog (role-based access, row/column-level policies)[1][5][10]Encryption of data in transit and at rest via underlying cloud provider primitives (KMS/Key Vault, TLS)[5][10]Governance features including lineage tracking, masking, and auditability built into lakehouse stack[1][10]
Architecture
Databricks runs a two-layer architecture where a Databricks-managed control plane hosts the UI, APIs, metadata, and orchestration services, while customer workloads execute in a compute/data plane inside the customer’s AWS, Azure, or GCP account (or Databricks’ serverless account) against cloud object storage using Spark and Delta Lake.[3][4][9][11] Around this core, Databricks positions an open-core lakehouse stack (Delta Lake, Spark, MLflow) with proprietary governance, serverless, and AI platform services, integrated into medallion-style production patterns.[1][2][6][12]
Data Platform
Proximity
Native (own / self-hosted weights)
Models
middleware / wrappersMeta · LlamaOpenAI · GPT
Cloud · datastore
Multi-CloudProprietary multi-cluster shared-data columnar engine on cloud object storage (Amazon S3, Azure Blob Storage, Google Cloud Storage)[1][16][19]Service-oriented cloud services layer for metadata, transactions, optimization, authentication, and coordination[1][16][19]Massively parallel processing virtual warehouses as independent compute clusters[14][16][19]
Compliance
Role-based access control and fine-grained security policies in cloud services layer[1][16]Encryption of data at rest and in transit on public cloud object storage (S3/Blob/GCS)[1][16]Secure data sharing and governance features integrated into the services layer[1][11][16]
Architecture
Snowflake runs a proprietary three-layer, multi-cluster shared-data architecture on AWS, Azure, and GCP, separating compressed columnar storage on cloud object stores from MPP compute warehouses and a distributed cloud services control plane.[1][16][19] Internally it is a service-oriented system with independently scalable storage, compute, and metadata/transaction services built for OLAP workloads.[16][19]